Modeling Family Behaviors in Crowd Simulation

Modeling human behavior for a general situation is difficult, if not impossible. Crowd simulation represents one of the approaches most commonly used to model such behavior. It is mainly concerned with modeling the different human structures incorporated in a crowd. These structures could comprise individuals, groups, friends, and families. Various instances of these structures and their corresponding behaviors are modeled to predict crowd responses under certain circumstances and to subsequently improve event management, facility and emergency planning. Most currently existing modeled behaviors are concerned with depicting individuals as autonomous agents or groups of agents in certain environments. This research focuses on providing structural and state-based behavioral models for the concept of a family incorporated in the crowd. The structural model defines parents, teenagers, children, and elderly as members of the family. It also draws on the associated interrelationships and the rules that govern them. The behavioral model of the family encompasses a number of behavioral models associated with the triggering of certain well-known activities that correspond to the family’s situation. For instance, in normal cases, a family member(s) may be hungry, bored, or tired, may need a restroom, etc. In an emergency case, a family may experience the loss of a family member(s), the need to assist in safe evacuation, etc. Activities that such cases trigger include splitting, joining, carrying children, looking for family member(s), or waiting for them. The proposed family model is implemented on top of the RVO2 library that is using agent-based approach in crowd simulation. Simulation case studies are developed to answer research questions related to various family evacuation approaches in emergency situations

Real-time simulation of crowds of heterogeneous agents

Behavioural simulation of agents representing humanoid characters has spread to many areas in recent years. A part of such simulations are crowd simulations, where large numbers of agents move and interact at the same time. Finding a suitable level of individual agent complexity so that large simulations are possible and suitable behaviour is reached, is challenging. In addition, executing such a simulation in real-time is problematic. In my work I developed a real-time application in Unity game engine which makes use of a number of main techniques and approaches for heterogeneous crowd simulations, such as modular architecture, environment sensing, obstacle avoidance, finite state machines for behaviour modeling, animator for animation visualisation etc. I thoroughly described and presented those approaches and techniques and commented on the results obtained in several different scenes which represent specific real-world situations

Simulating human-like behaviour in games using intelligent agents

The field of artificial intelligence has come a long way in the past 50 years, and studies of its methods soon expanded to a field in which they are of great practical value -- computer games. The concept of intelligent agents provides a much needed theoretical background for the comparison of various different approaches to intelligent, rational behaviour of computer-controlled characters in games. By combining rationality with certain limitations of the capabilities of our agents, we can achieve very human-like behaviour. In this diploma thesis we introduced and compared various types of agents that are used in games (but not only in games) and showed how to implement meaningful, reasonable limitations to agent capabilities into the game world. The aim of this thesis is to show the strengths and weaknesses of each type of agent and decide what types of tasks it is suitable for. We showed that even the simplest agents can succeed in their tasks in certain task environments, while more difficult task environments often require a more advanced agent architecture. The addition of goals into the agent architecture had the biggest impact on the agent's behaviour, while the finite-state machine approach kept our implementation simple and compact

Individual and group dynamic behaviour patterns in bound spaces

The behaviour analysis of individual and group dynamics in closed spaces is a subject of extensive research in both academia and industry. However, despite recent technological advancements the problem of implementing the existing methods for visual behaviour data analysis in production systems remains difficult and the applications are available only in special cases in which the resourcing is not a problem. Most of the approaches concentrate on direct extraction and classification of the visual features from the video footage for recognising the dynamic behaviour directly from the source. The adoption of such an approach allows recognising directly the elementary actions of moving objects, which is a difficult task on its own. The major factor that impacts the performance of the methods for video analytics is the necessity to combine processing of enormous volume of video data with complex analysis of this data using and computationally resourcedemanding analytical algorithms. This is not feasible for many applications, which must work in real time. In this research, an alternative simulation-based approach for behaviour analysis has been adopted. It can potentially reduce the requirements for extracting information from real video footage for the purpose of the analysis of the dynamic behaviour. This can be achieved by combining only limited data extracted from the original video footage with a symbolic data about the events registered on the scene, which is generated by 3D simulation synchronized with the original footage. Additionally, through incorporating some physical laws and the logics of dynamic behaviour directly in the 3D model of the visual scene, this framework allows to capture the behavioural patterns using simple syntactic pattern recognition methods. The extensive experiments with the prototype implementation prove in a convincing manner that the 3D simulation generates sufficiently rich data to allow analysing the dynamic behaviour in real-time with sufficient adequacy without the need to use precise physical data, using only a limited data about the objects on the scene, their location and dynamic characteristics. This research can have a wide applicability in different areas where the video analytics is necessary, ranging from public safety and video surveillance to marketing research to computer games and animation. Its limitations are linked to the dependence on some preliminary processing of the video footage which is still less detailed and computationally demanding than the methods which use directly the video frames of the original footage